DM-JEPA 1.1
A 308M non-autoregressive choice model: a ModernBERT-base encoder reads up to 16K tokens of state, a small latent predictor scores each option, and a softmax gives a probability per option in one forward pass. Needs the custom code in its repo; weak on held-out tasks.
Danger Labs calls it a JEPA-style "System 1" decision engine. A ModernBERT-base encoder, with RoPE stretched to 16,384 tokens of state and 512 per option, embeds the state and each option; a four-step recurrent predictor scores each option, and a temperature-scaled cosine softmax returns a probability per option. Choice questions only. The card says "calibrated" but publishes no calibration error. It will not load through transformers AutoModel: it needs modeling_dm_jepa.py and the djepa/ package in the repo, plus the ModernBERT-base tokenizer. The maker reports its own run of the Decision Index 0.2.1 protocol: 23.16 balanced skill and 41.92 raw over 150,759 requests. The same card claims 100% on GSM8K and on an "OpenJev High-Trust" suite, which sit oddly with that overall score. The third-party Decision Index 0.3 board scores DM-JEPA 1.1 at 4.05 (rank 105), and 2.84 on new domains.
What it decides
- choice — picks one option from a set
At a glance
| Parameters | 308M |
| Base model | answerdotai/ModernBERT-base |
| Maker | Danger Labs |
| Released | 2026-10-04 |
| License | mit |
| Reported latency | 34.3 ms median device-synchronised forward pass on one NVIDIA GB10 |
Get the weights
pip install systemonemodels
systemone pull danger-labs/dm-jepa
The files are served from the maker's Hugging Face repository, DangerLabs/DM-JEPA, and verified against the checksums recorded here.
Read more
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